In this paper, a new radio resource management algorithm is proposed which aims the reduction of supply power consumption at the base station for multi-user MIMO-OFDM. The proposed
On the basis of ensuring smooth user communication and normal operation of base stations, it realizes orderly regulation of energy storage for large-scale base stations,
The energy storage of base station has the potential to promote frequency stability as the construction of the 5G base station accelerates. This paper proposes a control strategy
Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a
Modern base station power profiles (e.g., [15]) usually include such limitations, and should be used when validating the algorithms performance. When considering faster
Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper.
However, the deployment of numerous small cells results in a linear increase in energy consumption in wireless communication systems. To enhance system efficiency and
A base station control algorithm based on Multi-Agent Proximity Policy Optimization (MAPPO) is designed. In the constructed 5G UDN model, each base station is considered as
This paper proposed a multi-agent reinforcement learning based power control strategy for base stations in UDN. The method initially modeled system energy consumption
This article focuses on the optimized operation of communication base stations, especially the effective utilization of energy storage batteries. Currently, base station energy
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